Combating Hypertension Beyond Genome-Wide Association Studies: Microbiome and Artificial Intelligence As Opportunities for Precision Medicine

Authors

Sachin Aryal
Ishan Manandhar
Xue Mei
Beng S Yeoh
Ramakumar Tummala
Piu Saha
Islam Osman
Jasenka Zubcevic
David J Durgan
Matam Vijay-Kumar
Bina Joe

Publication Date

1-1-2023

Journal

Cambridge Prisms: Precision Medicine

DOI

10.1017/pcm.2023.13

PMID

38550938

PMCID

PMC10953772

PubMedCentral® Posted Date

5-16-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Machine learning, high blood pressure, gut microbiota, genome-based risk scores, personalized medicine

Abstract

The single largest contributor to human mortality is cardiovascular disease, the top risk factor for which is hypertension (HTN). The last two decades have placed much emphasis on the identification of genetic factors contributing to HTN. As a result, over 1,500 genetic alleles have been associated with human HTN. Mapping studies using genetic models of HTN have yielded hundreds of blood pressure (BP) loci but their individual effects on BP are minor, which limits opportunities to target them in the clinic. The value of collecting genome-wide association data is evident in ongoing research, which is beginning to utilize these data at individual-level genetic disparities combined with artificial intelligence (AI) strategies to develop a polygenic risk score (PRS) for the prediction of HTN. However, PRS alone may or may not be sufficient to account for the incidence and progression of HTN because genetics is responsible for <30% of the risk factors influencing the etiology of HTN pathogenesis. Therefore, integrating data from other nongenetic factors influencing BP regulation will be important to enhance the power of PRS. One such factor is the composition of gut microbiota, which constitute a more recently discovered important contributor to HTN. Studies to-date have clearly demonstrated that the transition from normal BP homeostasis to a state of elevated BP is linked to compositional changes in gut microbiota and its interaction with the host. Here, we first document evidence from studies on gut dysbiosis in animal models and patients with HTN followed by a discussion on the prospects of using microbiota data to develop a metagenomic risk score (MRS) for HTN to be combined with PRS and a clinical risk score (CRS). Finally, we propose that integrating AI to learn from the combined PRS, MRS and CRS may further enhance predictive power for the susceptibility and progression of HTN.

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